29th March 2023
Data Scientist
Big Data
London
£75k per annum
Mid-level Data Scientist
Hybrid, London.
75k per annum.
Spencer Rose are looking for a Mid-level Data Scientist to join our client’s world-class Data Science team. Our client aims to support the commercial insurance industry by harnessing the Internet of Things (IoT) and vast datasets to uncover insights improving risk selection, pricing, and management and ensuring a sustainable future for the sector.
This role involves developing products that solve real problems for customers. The Data Scientist will to help shape their dynamic data platform that’s changing the game in commercial insurance.
Data Scientist Responsibilities:
- Work in a multi-disciplined team to deliver projects to an agreed schedule according to the business roadmap.
- Analyse and model structured and unstructured data using advanced statistical methods.
- Perform full modelling projects to create linear and nonlinear models, including: data ingestion, feature creation and selection, cross-validation, hyperparameter tuning and model assessment.
- Implement algorithms and software needed to perform analysis.
- Research and evaluate new approaches to derive benefit from the significant data assets available.
- Collaborate with Product teams to drive new product ideas and increase the value of data science within the product and organisation.
Data Scientist Skills:
- Excellent self-management and prioritisation skills, and proven track record of delivering complex projects to deadlines.
- Strong data wrangling and processing skills along with a background in analysis and predictive modelling.
- Extensive experience with supervised classification and regression modelling using linear and nonlinear techniques.
- Knowledge of the full end-to-end modelling life cycle from feature creation, selection, model-building, hyperparameter optimisation and model assessment.
- Strong Python skills and exposure to other programming languages.
- Apache Spark (preferably with PySpark) and Python pandas, numpy, scikit-learn.
- Supervised Learning (Regression, Classification), Unsupervised (Clustering), Dimensionality Reduction, Model Selection and Optimisation, Feature Selection, Metric Selection, Bootstrapping, Ensembling & Stacking Methods
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